Bioinformatics is the use of mathematical, statistical and computer methods to analyze biological, biochemical, and biophysical data. Because bioinformatics is a young, rapidly evolving field, however, it also has a number of other credible definitions. It can also be defined as the science and technology of learning, managing, and processing biological information. Bioinformatics is often focused on obtaining biologically oriented data, organizing this information into databases, developing methods to get useful information from such databases, and devising methods to integrate related data from disparate sources. The computer databases and algorithms are developed to speed up and enhance biological research.
Bioinformatics can help answer such questions as whether a newly analyzed gene is similar to any previously known gene, whether a protein's sequence can suggest how the protein functions, and whether the genes turned on in a cancer cell are different from those turned on in a healthy cell.
Databases and Analysis Programs
A good deal of the early work in bioinformatics focused on processing and analyzing gene and protein sequences catalogued in databases such as GenBank, EMBL, and SWISS-PROT. Such databases were developed in academia or by government-sponsored groups and served as repositories where scientists could store and share their sequence data with other researchers. With the start of the Human Genome Project in 1990, efforts in bioinformatics intensified, rising to the challenge of handling the large amounts of DNA sequence data being generated at an unprecedented rate. By the midto late-1990s, much of the efforts in bioinformatics centered around genomic data, generated by the Human Genome Project and by private companies, and around proteomic data.
Early analysis of sequence information focused on looking for similarities between genes and between proteins. Algorithms were developed to help researchers rapidly identify similar gene or protein sequences. Such tools were extremely useful for determining whether a newly sequenced piece of DNA was at all similar to sequences already entered in a database. To determine how multiple sequences align and to view their similarities, multiplealignment programs were developed. Such programs helped scientists compare the sequences of closely related genes or compare the sequence of a particular gene or protein as it appears in several species.
To better understand the functional roles of new nucleotide and amino acid sequences, researchers developed algorithms to look for particular sequence "domains." Domains are regions where a particular sequence of nucleotides or amino acids is indicative of function in the protein. For example, a protein may have a domain that binds to ATP or GTP, two important protein regulators.
In addition, these algorithms can detect sequences that denote a region involved in particular types of post-translational modifications, such as tyrosine phosphorylation. Tools such as prosite, blocks, prints, and Pfam can be used to detect and predict such protein domains in sequence data.
Structure is central to protein function, and another set of tools, including SWISS-MODEL, allows researchers to use gene and protein sequence data to predict a protein's three-dimensional structure. Such tools can help predict how mutations in a gene sequence could alter the three-dimensional structure of the corresponding protein. They accomplish such molecular modeling by comparing a novel sequence to the sequences of genes whose protein structures are known.
The majority of tools were developed as academic freeware distributed on the Internet. In the early-to mid-1990s, commercial companies began to develop their own proprietary algorithms and tools, as well as their own proprietary databases. Those databases were then marketed to pharmaceutical and biotech companies as well as to academic research groups. The most commercially viable and profitable businesses focused on the production and sale of proprietary DNA-and gene-sequence databases in the mid-to late-1990s. These databases primarily contained genetic information that were not in the public domain databases, such as GenBank, and they thus offered potential competitive advantages to the drug discovery groups of large pharmaceutical and biotech companies.
Applications of Bioinformatics to Drug Discovery
The application of bioinformatics to genomics data could be a huge potential boon for the discovery of new drugs. During the 1990s many pharmaceutical companies and biotech companies became convinced that they could speed up their drug-discovery pipelines by taking advantage of the data from the Human Genome Project as well as by funding their own internal genomics programs and by collaborating with third-party genomics companies.
The goal in such practical applications is to use such data as DNA sequence information and gene expression levels to help discover new drug targets. The vast majority of drugs target proteins, but there are a handful of drugs, such as some chemotherapeutic agents, that bind to DNA. In cases where the target is a protein, the drugs themselves are primarily small chemical molecules or, in some cases, small proteins, such as hormones, that bind to a larger protein in the body. Some drugs are therapeutic proteins delivered to the site of the disease.
The extent to which genomics will actually be able to help identify validated drug targets is uncertain. Genomics and bioinformatics are still young areas, and the drug development cycle can take up to ten years. As of 2001 relatively few of the drugs on the market or in the late stages of clinical trials were discovered via genomics or bioinformatics programs.
Specialists
Bioinformatics is applied to at least five major types of activities: data acquisition, database development, data analysis, data integration, and analysis of integrated data.
Data Acquisition
Data acquisition is primarily concerned with accessing and storing data generated directly off of laboratory instruments. Many of these instruments are either automated or semi-automated high-throughput instruments that generate large volumes of data. The Human Genome Project utilized hundreds of DNA sequencers, producing enormous amounts of data. The data had to be captured in the appropriate format, and it had to be capable of being linked to all the information related to the DNA samples, such as the species, tissue type, and quality parameters used in the experiments. This area of bioinformatics primarily relates to the use of "laboratory information management systems," which are the computer systems used to manage the information needs of a particular laboratory.
Database Development
Many laboratories generate large volumes of such data as DNA sequences, gene expression information, three-dimensional molecular structure, and high-throughput screening. Consequently, they must develop effective databases for storing and quickly accessing data. For each type of data, it is likely that a different database organization must be used. A database must be designed to allow efficient storage, search, and analysis of the data it contains. Designing a high-quality database is complicated by the fact that there are several formats for many types of data and a wide variety of ways in which scientists may want to use the data. Many of these databases are best built using a relational database architecture, often based on Oracle or Sybase.
A strong background in relational databases is a fundamental requirement for working in database development. Having some background in the molecular biology techniques used to generate the data is also important. Most critical for the bioinformatics specialist is to have a strong working relationship with the researchers who will be using the database and the ability to understand and interpret their needs into functional database capabilities.
Data Analysis
Being able to analyze data efficiently requires having a good database design, allowing researchers to query the database effectively and letting them quickly obtain the types of information they need to begin their data analysis. If queries cannot be performed, or if performance is tediously slow, the whole system breaks down, since scientists will not be inclined to use the database. Once data is obtained from the database, the user must be able to easily transform it into the format appropriate for the desired analysis tools.
This can be challenging, since researchers often use a combination of publicly available tools, tools developed in-house, and third-party commercial tools. Each tool may have different input and output formats. Starting in the late 1990s, there have been both commercial and in-house efforts at pharmaceutical and biotech companies to reduce the formatting complexities. Such simplification efforts focus on building analysis systems with a number of tools integrated within them such that the transfer of data between tools appears seamless to the end user.
Bioinformatics analysts have a broad range of opportunities. They may write specific algorithms to analyze data, or they may be expert users of analysis tools, helping scientists understand how the tools analyze the data and how to interpret results. A knowledge of various programming languages, such as Java, PERL, C, C++, and Visual Basic, is very useful, if not required, for those working in this area.
Data Integration
Once information has been analyzed, a researcher often needs to associate or integrate it with related data from other databases. For example, a scientist may run a series of gene expression analysis experiments and observe that a particular set of 100 genes is more highly expressed in cancerous lung tissue than in normal lung tissue. The scientist might wonder which of the genes is most likely to be truly related to the disease. To answer the question, the researcher might try to find out more information about those 100 genes, including any associated gene sequence, protein, enzyme, disease, metabolic pathways, or signal transduction pathway data.
Such information will help the researcher narrow the list down to a smaller set of genes. Finding this information, however, requires connections or links between the different databases and a good way to present and store the information. An understanding of database architectures and the relationship between the various biological concepts in the databases is key to doing effective data integration.
Analysis of Integrated Data
Once various types of data are integrated, users need a good way to present these various pieces of data so they can be interpreted and analyzed. The information should be capable of being stored and retrieved so that, over time, various pieces of information can be combined to form a "knowledge base" that can be extended as more experiments are run and additional data are integrated from other sources. This type of work requires skills related to database design and architecture. It also requires specific programming skills in various computer languages, as well as expertise in developing interfaces between a computer and its user.
Bibliography
Howard, Ken. "The Bioinformatics Gold Rush." Scientific American 283, no. 1 (2000): 58-64.
Internet Resources
"EID V3 N3: Host Genes and HIV." Centers for Disease Control and Prevention. http://www.cdc.gov/ncidod/eid/vol3no3/smith.htm.
EMBL Nucleotide Sequence Database. Release 69. December 2001. European Bioinformatics Institute. http://www.ebi.ac.uk/.
GenBank. National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov/.
SWISS-PROT. Swiss Institute of Bioinformatics. http://www.expasy.org/sprot/.
—Anthony J. Recupero
No comments:
Post a Comment